The $k$-coloring fitness landscape
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
The $k$-coloring fitness landscape
Author(s) :
Bouziri, Hind [Auteur]
Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus [LARODEC]
Mellouli, Khaled [Auteur]
Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus [LARODEC]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus [LARODEC]
Mellouli, Khaled [Auteur]
Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus [LARODEC]
Talbi, El-Ghazali [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Journal title :
Journal of Combinatorial Optimization
Pages :
306-329
Publisher :
Springer Verlag
Publication date :
2011
ISSN :
1382-6905
English keyword(s) :
$k$-coloring
Fitness landscape
Distance
Distribution of solutions
Time series
Fitness landscape
Distance
Distribution of solutions
Time series
HAL domain(s) :
Computer Science [cs]/Operations Research [math.OC]
English abstract : [en]
This paper deals with the fitness landscape analysis of the $k$-coloring problem. We study several standard instances extracted from the second DIMACS benchmark. Statistical indicators are used to investigate both global ...
Show more >This paper deals with the fitness landscape analysis of the $k$-coloring problem. We study several standard instances extracted from the second DIMACS benchmark. Statistical indicators are used to investigate both global and local structure of fitness landscapes. An approximative distance on the $k$-coloring space is proposed to perform these statistical measures. Local search operator trajectories on various landscapes are then studied using the time series analysis. Results are used to better understand the behavior of metaheuristics based on local search when dealing with the graph coloring problem.Show less >
Show more >This paper deals with the fitness landscape analysis of the $k$-coloring problem. We study several standard instances extracted from the second DIMACS benchmark. Statistical indicators are used to investigate both global and local structure of fitness landscapes. An approximative distance on the $k$-coloring space is proposed to perform these statistical measures. Local search operator trajectories on various landscapes are then studied using the time series analysis. Results are used to better understand the behavior of metaheuristics based on local search when dealing with the graph coloring problem.Show less >
Language :
Anglais
Popular science :
Non
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